double luT( viennacl::matrix<T> &vclX, viennacl::vector_base<T> &vclD) { double logdet=-99.9; viennacl::linalg::lu_factorize(vclX); // pointer to the actual diagonal viennacl::vector_base<T> diagOfVar( vclX.handle(), vclX.size1(), 0, vclX.internal_size2() + 1); // compute log determinant vclD = viennacl::linalg::element_log(diagOfVar); logdet = viennacl::linalg::sum(vclD); // OPERATION_UNARY_LOG_TYPE //http://viennacl.sourceforge.net/doc/scheduler_8cpp-example.html#a11 // put the diagonals in D, and 1's on the diagonal of L vclD = diagOfVar; //diagOfVar = T(1); // problem here return(logdet); }
void init_random(viennacl::matrix<T, F> & M) { std::vector<T> cM(M.internal_size()); for (std::size_t i = 0; i < M.size1(); ++i) for (std::size_t j = 0; j < M.size2(); ++j) cM[F::mem_index(i, j, M.internal_size1(), M.internal_size2())] = T(rand())/T(RAND_MAX); viennacl::fast_copy(&cM[0],&cM[0] + cM.size(),M); }
bool householder_c(viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT>& A, viennacl::matrix<SCALARTYPE, row_major, ALIGNMENT>& Q, viennacl::vector<SCALARTYPE, ALIGNMENT>& D, vcl_size_t row_start, vcl_size_t col_start) { viennacl::ocl::context & ctx = const_cast<viennacl::ocl::context &>(viennacl::traits::opencl_handle(A).context()); if (row_start + 1 >= A.size1()) return false; prepare_householder_vector(A, D, A.size1(), row_start, col_start, row_start, true); { viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::svd<SCALARTYPE>::program_name(), SVD_HOUSEHOLDER_UPDATE_A_LEFT_KERNEL); viennacl::ocl::enqueue(kernel( A, D, static_cast<cl_uint>(row_start), static_cast<cl_uint>(col_start), static_cast<cl_uint>(A.size1()), static_cast<cl_uint>(A.size2()), static_cast<cl_uint>(A.internal_size2()), viennacl::ocl::local_mem(static_cast<cl_uint>(128 * sizeof(SCALARTYPE))) )); } { viennacl::ocl::kernel& kernel = ctx.get_kernel(viennacl::linalg::opencl::kernels::svd<SCALARTYPE>::program_name(), SVD_HOUSEHOLDER_UPDATE_QL_KERNEL); viennacl::ocl::enqueue(kernel( Q, D, static_cast<cl_uint>(A.size1()), // static_cast<cl_uint>(A.size2()), static_cast<cl_uint>(Q.internal_size2()), viennacl::ocl::local_mem(static_cast<cl_uint>(128 * sizeof(SCALARTYPE))) )); } return true; }
NumericT diff(std::vector<std::vector<NumericT> > const & A1, viennacl::matrix<NumericT> const & A2) { std::vector<NumericT> host_values(A2.internal_size()); for (std::size_t i=0; i<A2.size1(); ++i) for (std::size_t j=0; j<A2.size2(); ++j) host_values[i*A2.internal_size2() + j] = A1[i][j]; std::vector<NumericT> device_values(A2.internal_size()); viennacl::fast_copy(A2, &device_values[0]); viennacl::vector<NumericT> vcl_device_values(A2.internal_size()); // workaround to avoid code duplication viennacl::copy(device_values, vcl_device_values); return diff(host_values, vcl_device_values); }
void nmf(viennacl::matrix<ScalarType> const & v, viennacl::matrix<ScalarType> & w, viennacl::matrix<ScalarType> & h, std::size_t k, ScalarType eps = 0.000001, std::size_t max_iter = 10000, std::size_t check_diff_every_step = 100) { viennacl::linalg::kernels::nmf<ScalarType, 1>::init(); w.resize(v.size1(), k); h.resize(k, v.size2()); std::vector<ScalarType> stl_w(w.internal_size1() * w.internal_size2()); std::vector<ScalarType> stl_h(h.internal_size1() * h.internal_size2()); for (std::size_t j = 0; j < stl_w.size(); j++) stl_w[j] = static_cast<ScalarType>(rand()) / RAND_MAX; for (std::size_t j = 0; j < stl_h.size(); j++) stl_h[j] = static_cast<ScalarType>(rand()) / RAND_MAX; viennacl::matrix<ScalarType> wn(v.size1(), k); viennacl::matrix<ScalarType> wd(v.size1(), k); viennacl::matrix<ScalarType> wtmp(v.size1(), v.size2()); viennacl::matrix<ScalarType> hn(k, v.size2()); viennacl::matrix<ScalarType> hd(k, v.size2()); viennacl::matrix<ScalarType> htmp(k, k); viennacl::matrix<ScalarType> appr(v.size1(), v.size2()); viennacl::vector<ScalarType> diff(v.size1() * v.size2()); viennacl::fast_copy(&stl_w[0], &stl_w[0] + stl_w.size(), w); viennacl::fast_copy(&stl_h[0], &stl_h[0] + stl_h.size(), h); ScalarType last_diff = 0.0f; for (std::size_t i = 0; i < max_iter; i++) { { hn = viennacl::linalg::prod(trans(w), v); htmp = viennacl::linalg::prod(trans(w), w); hd = viennacl::linalg::prod(htmp, h); viennacl::ocl::kernel & mul_div_kernel = viennacl::ocl::get_kernel(viennacl::linalg::kernels::nmf<ScalarType, 1>::program_name(), NMF_MUL_DIV_KERNEL); viennacl::ocl::enqueue(mul_div_kernel(h, hn, hd, cl_uint(stl_h.size()))); } { wn = viennacl::linalg::prod(v, trans(h)); wtmp = viennacl::linalg::prod(w, h); wd = viennacl::linalg::prod(wtmp, trans(h)); viennacl::ocl::kernel & mul_div_kernel = viennacl::ocl::get_kernel(viennacl::linalg::kernels::nmf<ScalarType, 1>::program_name(), NMF_MUL_DIV_KERNEL); viennacl::ocl::enqueue(mul_div_kernel(w, wn, wd, cl_uint(stl_w.size()))); } if (i % check_diff_every_step == 0) { appr = viennacl::linalg::prod(w, h); viennacl::ocl::kernel & sub_kernel = viennacl::ocl::get_kernel(viennacl::linalg::kernels::nmf<ScalarType, 1>::program_name(), NMF_SUB_KERNEL); //this is a cheat. i.e save difference of two matrix into vector to get norm_2 viennacl::ocl::enqueue(sub_kernel(appr, v, diff, cl_uint(v.size1() * v.size2()))); ScalarType diff_val = viennacl::linalg::norm_2(diff); if((diff_val < eps) || (fabs(diff_val - last_diff) < eps)) { //std::cout << "Breaked at diff - " << diff_val << "\n"; break; } last_diff = diff_val; //printf("Iteration #%lu - %.5f \n", i, diff_val); } } }
viennacl::vector_range<viennacl::vector_base<T> > sharedCol(){ // viennacl::vector_base<T> tmp(ptr_matrix->handle(), ptr_matrix->internal_size(), 0, 1); // std::cout << "returning column" << std::endl; viennacl::vector_base<T> tmp(ptr_matrix->handle(), ptr_matrix->size1(), begin, ptr_matrix->internal_size2()); // std::cout << "got column" << std::endl; viennacl::vector_range<viennacl::vector_base<T> > v_sub(tmp, r); // std::cout << "got range" << std::endl; return v_sub; }